Energy analytics learning machine

ABSTRACT

Energy Analytics Learning Machine (or EALM) system is a machine learning based, “brutally empirical” analysis system for use in optimizing the payout from one or more energy sources. EALM system optimizes exploration, production, distribution and/or consumption of an energy source while minimizing costs to the producer, transporter, refiner and/or consumer. Normalized data are processed to determine clusters of correlation in multi-dimensional space to identify a machine learned ranking of importance weights for each attribute. Predictive and prescriptive optimization on the normalized energy data is performed utilizing unique combinations of machine learning and statistical algorithm ensembles. The unstructured textual energy data are classified to correlate with optimal production to capture the dynamics of one or more energy sources of physically real or theoretically calculated systems to provide categorization results from labeled data sets to identify patterns.

RELATED APPLICATION

The present application claims a priority to U.S. Provisional PatentApplication Ser. No. 62/350,663 filed Jun. 15, 2016, which isincorporated herein by reference in its entirety.

FIELD OF THE INVENTION

The present invention relates to a petroleum analytics learning machinesystem and method to maximize production from wells of oil and naturalgas fields while minimizing costs.

OBJECT AND A SUMMARY OF THE INVENTION

The Petroleum Analytics Learning Machine (PALM) is a machine learningbased, “brutally empirical” analysis system for use in all upstream andmidstream oil and gas operations. The Petroleum Analytics LearningMachine™ is a trademark of applicant. The objective of the PALM is tobecome the go-to ‘brain’ of oil and gas exploration and production,including drilling, completion, and pipeline gathering operations. ThePALM was reduced to practice primarily in the new unconventional shaleoil and gas play. The PALM analyzed more than 100 attributes integratedfrom all available data referenced above, in more than 150 horizontalwells and more than 2000 hydraulic fracture (frac) stages that weredrilled since 2012 in the wet gas region of the Target Layer shale ofPennsylvania. The PALM was also validated in more than 3000 shale oilwells with more than 10,000 hydraulic fracture stages in the PermianBasin of Texas. In accordance with an exemplary embodiment of theclaimed invention, The PALM comprises Machine Analytics Products™ (MAP)Application subsystems (subsystems) that are big-data-centric, usingcomputational machine learning predictive and prescriptive analysistechniques to maximize production of hydrocarbons while minimizing costsof oil and gas upstream exploration and production (E&P) and midstreampipeline operations.

In accordance with an exemplary embodiment of the claimed invention, thePALM comprises MAP subsystems for geology, geophysics, reservoirmodeling and rock physics, MAPGEORES; drilling, MAPDRILL; hydraulicfracturing and completions, MAPFRAC; production of hydrocarbonsincluding oil and other liquid condensates, natural gas, and water,MAPPROD; and gathering pipelines and compressor stations, MAPGATHER. Inaccordance with an aspect of the claimed invention, PALM furthercomprises other MAP subsystems, such as portfolio management,MAPPORTFOLIO; and others subsystems specifically developed for acustomer and the like. These subsystems use the PALM System IntegrationDatabase (SID) to retrieve integrated data, then perform machinelearning and other statistical analyses of that data, and return to theSID results of computation and predictive and prescriptive actions thatcan be forwarded by the TOTALVU user interface (UI) to controllers,human and/or automated, so that real-time optimization of production andminimization of costs can be realized for new wells. The unique PALMproduct suite was developed by inventing scientists and engineers withmore than 80 years of combined energy industry expertise, workingalongside big data scientists experienced in building real-time decisionand control systems. The PALM predictive and prescriptive technologiesutilize Support Vector Machine learning, time-series shape recognition,and real-time Random Forest and decision trees to steer hydraulicfractures to become more likely high instead of low producers, stage bystage, as completions of horizontal and vertical shale wells progress.The PALM also uses Support Vector Regression, logistic regression,Bayesian models, nearest neighbors, neural networks and deep learningnetworks uniquely combined as ensemble learning to weigh the importanceof hundreds to thousands of geological, geophysical, and engineeringattributes, both measured in the field and computed from theoreticalanalyses such as reservoir simulation models and 4D seismic and gravitygradiometry monitoring of production changes over time.

In accordance with an exemplary embodiment of the claimed invention, asystem and method for optimizing exploration, production and gatheringfrom at least one well to all wells of oil and natural gas fields usinga Petroleum Analytics Learning Machine system to maximize productionwhile minimizing costs is provided. Structured digital data andunstructured textual data from geological, geophysical, reservoirmodeling simulation, drilling, hydraulic fracturing and completion, andproduction of crude oil, natural gas, ethane, butane, propane andcondensates are collected. Incoming data over a communications networkare received and stored into a system integration database by aprocessor-based server or cloud-based distribution of servers to providecollected data for analyses. The incoming data comprises digitalexogenous data, real-time and historical endogenous data, historicaldata from surrounding production wells, hydraulic fracture completiondata, and progress, status and maintenance data from new vertical andhorizontal wells, including kickoffs, sidetracks, step-outs, pipelinegathering systems, compressor stations and other kinds of oil and gassensor data including from public and private data sources now existentand of future design. The time and depth for each data point of thecollected data are recorded. The collected data are ‘cleaned’ toeliminate extraneous and noisy data. The cleaned data are normalized andstored. The normalized data are processed to determine clusters ofcorrelation in multi-dimensional space to identify a machine learnedranking of Importance Weights for each attribute. The Importance Weightsare convolved with specific well weights to identifying patterns toenhance production of at least one well or all wells of oil and naturalgas fields.

In accordance with an exemplary embodiment of the claimed invention,predictive and prescriptive optimization are performed on the normalizeddata utilizing unique combinations of machine learning and statisticalalgorithm ensembles. The ensembles include at least two of thefollowing: linear and non-linear support vector machines andregressions, naïve Bayes, logistic regression, decision trees, hiddenMarkov models, random forests, gradient boosting machines, neuralnetworks, deep learning networks, among other machine learning models

In accordance with an exemplary embodiment of the claimed invention,unstructured textual data are classified to correlate with optimalproduction by utilizing progressive clustering using region growing fromlearned seeds, information extraction and retrieval, image recognition,textual mining, keyword and key phrase extraction, semantic andsentiment analysis, entity and pattern recognition and knowledgediscovery processing to capture the dynamics of said at least one or allwells of oil and natural gas fields. Categorization and classificationresults from labeled data sets to identify patterns are provided.

In accordance with an exemplary embodiment of the claimed invention,data and analyses are displayed, recommendations are transmitted, andactual field actions and reactions are received on a graphical userinterface on a network-enabled processing device over the communicationsnetwork. The recommendations are based on the collected data of one orall available wells, or one or more predicted conditions, communicationswith the one or more of the field systems is automatic, self-driving,autopilot and/or other autonomous means personalized to steer disparatedata simultaneously to operators working on vertical and horizontalwells, hydraulic fractures, or other field operations that are needed toimprove future production from wells in response to one or more detectedtrends. One or more predicted conditions, or prescriptiverecommendations are displayed on the graphical user interface connectedto the Petroleum Analytics Learning Machine system.

In accordance with an exemplary embodiment of the claimed invention, thePetroleum Analytics Learning Machine system utilizes an exploration andproduction numerical synthesizer of available data from wells in an areaor play, in order to score and rank the combined Importance Weights ofattributes to predict maximum production at minimum costs when convolvedwith specific attributes of each well. A real-time synthesizer of thePetroleum Analytics Learning Machine system optimizes drilling to matcha designed pathway of a drilled well including hitting one or moretarget landing zones, while minimizing sinuosity and optimallycompleting the hydraulic fracturing of horizontal, diagonal and/orvertical components of the drilled wells.

In accordance with an exemplary embodiment of the claimed invention, areal-time processor of the Petroleum Analytics Learning Machine systemconvolves importance weight values of attributes received by thePetroleum Analytics Learning Machine system from historical data andattribute data from each new well as it progresses in real time topredict future production of the new well before oil and gas aredelivered to the surface. The real-time processor utilizes time-seriesattributes during each hydraulic fracturing stage to automaticallyclassify production effectiveness of each hydraulic fracturing stage andto provide recommendations by self-driving, autopilot and/or otherautonomous means to maximize future production of each new well.Preferably, the recommendations are directed to optimization of theproduction of oil, natural gas, and natural gas liquids while minimizingwater production over time.

In accordance with an exemplary embodiment of the claimed invention, theaforesaid system and method receives data from digital field devicesinto the system integration database. The received data are combinedwith real time exogenous data comprising weather forecasts. Thehistorical data and the real-time data are fed into a data cleaningsystem to recognize a quality of the combination with the received datafrom a comparison with historical performance of at least one of eachdigital field device and a data stream. The system integration databaseretrieves, compares and combines geology and geophysics, reservoirmodeling, rock properties, drilling, completion, hydraulic fracturing,production and pipeline gathering data into a uniform data repository bylinking heterogeneous data sources with normalization based on commonunique identifiers. The common unique identifiers comprising at leastone of a well name, a well number, a region and geological location of awell, a well depth, time, and a physical property number or uniqueAmerican Petroleum Institute (API) number, and the geology andgeophysics, reservoir modeling, rock properties, drilling, hydraulicfracturing, completion, production, and pipeline gathering data.

In accordance with an exemplary embodiment of the claimed invention, theaforesaid system and method determines clusters of like correlations inone or more well conditions that will likely result in a productive wellusing the Petroleum Analytics Learning Machine system. The machinelearning predicted production volumes of hydrocarbon liquids, gases, andwater are generated for each well over time. Identified trends andpredicted production conditions are displayed. The Petroleum AnalyticsLearning Machine system alerts an operator when an anomaly between thepredicted production conditions and observed field conditions arise tomodify an estimated ultimate recovery.

In accordance with an exemplary embodiment of the claimed invention, thePetroleum Analytics Learning Machine system (PALM) has a coverage ofmultiple aspects in the analytics. The PALM utilizes at least one of thefollowing regressions: linear regression, support vector regression,classification, regression trees and random forests. The PALM utilizesat one of the following classification: logistic regression, supportvector machine and support vector regression, nearest neighbors,decision trees and random forest, neural networks and deep learningnetworks. The PALM utilizes at least one of the following clusteringmethods: k-means, k-medoids, expectation-maximization, agglomerativeclustering, and nonparametric Bayesian models. The PALM utilizes atleast one of the following feature selection and feature engineeringprocesses: information gain, chi-square, principle component analysis,and filter and wrapper feature selection methods. The PALM utilizes atleast one the following ensemble methods and models: bagging, boosting,gradient boosting machine, and random forests. The PALM utilizes atleast one of the following time series analyses: multivariate timeseries analysis, hidden Markov models, nonparametric Bayesian models.The PALM system utilizes at least one of the following large-scale orbig data analyses: autoregressive integrated moving average (ARIMA),multivariate time series analysis, hidden Markov models, nonparametricBayesian models, autoregressive conditional heteroskedasticity (ARCH),exponentially weighted moving average, and generalized autoregressiveconditional heteroskedasticity (GARCH). The PALM utilizes at least oneof the following large-scale or big data analyses: Hadoop MapReduce,Spark, approximation, and locality sensitivity hashing.

In accordance with an exemplary embodiment of the claimed invention, theaforesaid system and method recommends a shut-in, cessation orabandonment of a well in response to a determination by the PetroleumAnalytics Learning Machine system that anomalous conditions cannot beeconomically corrected.

In accordance with an exemplary embodiment of the claimed invention, theaforesaid system and method receives at least one of historicalexogenous data, real-time exogenous data and the real-time endogenousdata of said each well over a secure wireless or wired network. Thehistorical exogenous data and the real-time exogenous data include atleast one of historical weather data, forecast weather data, andproduction data from surrounding wells under similar historicalconditions; and computing forecast of future product for said each well.

In accordance with an exemplary embodiment of the claimed invention, theaforesaid system and method queries one or more system integrationdatabases of multiple surrounding wells in an area or querying oneintegrated master system integration database comprising regionallyrelevant geologic and geophysical data, reservoir models, drilling data,hydraulic fracturing data, the historical exogenous data, the real-timeexogenous data, and the real-time endogenous data to forecast productionof said each well.

In accordance with an exemplary embodiment of the claimed invention, theaforesaid exploration and production synthesizer of the PetroleumAnalytics Learning Machine system independently computes at least one ofthe following actions: steering of a new horizontal well within apreferred geological landing zone target, planning and execution of eachstage and perforation density and spacing, and a hydraulic fracturingdesign and sand proppant volume over time that positively affectsproduction decisions using real-time decision trees and random forestsduring each hydraulic fracture.

In accordance with an exemplary embodiment of the claimed invention, theaforesaid the exploration and production synthesizer of the PetroleumAnalytics Learning Machine system utilizes a support vector regressionto estimate relative importance weights of attributes inputted into thePetroleum Analytics Learning Machine system and a linear regression toassign a positive or negative correlation sign to product for eachweight. The attributes comprise: relevant geological and geophysicaldata; reservoir modeling results and calculations, including correctionfactors and assumptions; rock property measurements including poisonsratio, young's module, gamma ray radioactivity, organic and BritishThermal Unit (BTU) content; and combining parameters of the supportvector regression and linear regression to enable construction oftornado diagrams representing visually the importance weights of eachattribute that correlates with a positive production prediction resultand the importance weights of each attribute that correlates with anegative production prediction result for all wells in the area or play.

In accordance with an exemplary embodiment of the claimed invention, theaforesaid the real-time processor convolves f and g, where f is theimportance weight values of attributes computed by the PetroleumAnalytics Learning Machine system from historical data from all thewells in the area or play and g is each attribute value specific to awell as it progresses. The f*g is an integral transform of a product oftwo functions as attributes specific to said well, and the integraltransform predicts the future production of said well before the oil andgas are delivered to the surface.

In accordance with an exemplary embodiment of the claimed invention, theaforesaid system and method manages one or more prescriptive analyticscalculations to maximize production of liquids, and gases and tominimize production of water while minimizing the costs by theexploration and production synthesizer. The aforesaid exploration andproduction synthesizer computes multiple learning models operativelycoupled to the system integration database and receives collected datafrom the field in real time in an exit poll like voting procedure by thePetroleum Analytics Learning Machine system. The aforesaid system andmethod generates at least one predicted condition by the PetroleumAnalytics Learning Machine system, and stores resulting changes inoperations in the system integration database from field operations inresponse to a recommended action.

In accordance with an exemplary embodiment of the claimed invention, theaforesaid real-time synthesizer of the Petroleum Analytics LearningMachine system independently monitors drilling data. At least one of thefollowing surveys comprises the drilling data: measured depth,inclination, azimuth, total vertical depth, vertical steering, azimuthaldeparture and dog-leg severity, build rate and turn. At least one of thefollowing parameters comprises the drilling data: weight on bit, rotarytorque, circulation rate, measurement while drilling logs such as gammaray, density and electrical resistivity, differential pounds per squareinch, choke position, hook load, flow, alarm states, pump rates, pumpstrokes, inclination, rotary revolutions per minute, mud viscosity, mudweight, and deviation from a plan. At least one of the followingwellbore schematics comprises the drilling data: conductor casing depth,water casing depth, minimum casing depth, surface casing depth,production casing depth, float subs, float collars, float shoes, markerjoints, cement design, mud displacement volume, additive types, andadditive volumes. In accordance with an exemplary embodiment of theclaimed invention, the aforesaid system and method provides real-timerecommendations to minimize sinuosity of horizontal wells whilemaintaining a position within selected landing zones for predetermineddistances.

In accordance with an exemplary embodiment of the claimed invention, theaforesaid real-time processor independently monitors the completionsdata. The completions data comprises perforation depths and time,completions tool use and choke setting. Also, the completions datacomprises at least one of the following: time series hydraulic fracturedata including surface and downhole pressures, slurry compositions andwater mixes, sand volumes, breakdown pressure, proppant concentrationsand shut-in pressure for each hydraulic fracture. The aforesaid systemand method optimizes a maximum possible production from one or morehydraulic fracturing stages while minimizing its costs by a real-timeprocessing and generation of a predictive machine learning model basedon classification of the key attributes determined by the PetroleumAnalytics Learning Machine system. A time of a density drop that ends afirst sand injection is one of the key attributes. A pressure percentileat the time of the first density drop is one of the key attributes. Atime of a density drop that ends a second sand injection with sandlarger in diameter and heavier than the sand used in the first sandinjection is one of the key attributes. A pressure percentile at thetime of the second density drop is one of key attributes. A time of apressure drop at an end of a shut-in is one of the key attributes. Apressure percentile at the time of the pressure drop at the shut-in isone of the key attributes.

A time of a beginning of a sand change from a lighter to the heaviestsand is one of the key attributes. A pressure percentile at beginning ofa heaviest sand density increase is one of the key attributes. A time ofa highest pressure after the sand change to the heaviest sand is one ofthe key attributes. A pressure percentile of a maximum heaviest sandchange is one of the key attributes. A slope of a linear regression of apressure from beginning to end of the heaviest sand injection is one ofthe key attributes. An intercept of the linear regression of pressurefrom the beginning of the heaviest sand injection to the highestpressure at the end of the heaviest sand injection is one of the keyattributes. A scatter of the linear regression of the pressure from thebeginning of the heaviest sand injection to the highest pressure at theend of the heaviest sand injection is another of the key attributes.

In accordance with an exemplary embodiment of the claimed invention, theaforesaid real-time processor generates one or more real-time executablerecommendations to a hydraulic fracturing control center. The real-timeexecutable recommendations comprises at least one of the following: arecommended down-hole pressure, a proppant concentration, slurry rateand volume, and a water/sand mix based on at least trends in one or morehydraulic fracturing decision tree and random classifications ofhistorical, highly productive versus low producing stages.

In accordance with an exemplary embodiment of the claimed invention, theaforesaid system and method generates one or more conditions to changereal time decisions in the hydraulic fracturing control center based onupdated decision trees and random forest predictions that can steer inreal time towards a high producing fracture versus a low producingfracture stage.

In accordance with an exemplary embodiment of the claimed invention, theaforesaid real-time processor executes automated time seriesclassification using a machine learning feature recognition to developclusters of hydraulic fracture classes. The aforesaid real-timeprocessor correlates stages of each class to an average highestproduction of historical wells. The automated time series classificationcomprises multiple hydraulic fracture classifications. FracClass 1 is afailure to fracture due to surface equipment failures resulting in nohydraulic fracture and no input to a well production. FracClass 2 is ahydraulic fracture but a subsequent equipment failure either on thesurface or down-hole results in a minimal sand displacement and ahydraulic fracture is cut short by an operator, and a current stage iscancelled and moves on to a next stage in the well production plan.FracClass 3 is a successful fracture at extended time and cost, a rapidsand injection results in the well being accidentally packed-off to thesurface by an excessive sand buildup. A wellbore is cleanup with waterand re-perforated to allow a formation to take scheduled proppant sandsin FracClass 3. FracClass 4 is a successful fracture and injection of afull planned for amount of sand, but a late sand placement at an end ofa proppant injection results in a pressure surge. In FracClass 4, theheaviest sand injection sand placement is only pack-off locally to anear wellbore annulus of perforations of the current stage and asubsequent water cleanout fails to washout the near wellbore sandplacement away from the annulus. FracClass 5 is a perfect hydraulicfracture. In FracClass 5, the full planned amount of the sand isemplaced in a scheduled time, and a subsequent water wash successfullywashes the sand from the drill pipe, but also unfortunately theformation in the near wellbore, disrupting connectivity to the hydraulicfracture proppants deeper into the formation.

In accordance with an exemplary embodiment of the claimed invention, theaforesaid real-time processor performs the automated time seriesclassification by discovering sequential patterns and interactions amongtime series variables utilizing at least one of the following: anautoregressive integrated moving average (ARIMA) model, a multivariatetime series analysis, a hidden Markov model, an autoregressiveconditional heteroskedasticity (ARCH) model, an exponentially weightedmoving average and a generalized autoregressive conditionalheteroskedasticity (GARCH) model

In accordance with an exemplary embodiment of the claimed invention, theaforesaid real-time processor generates one or more executablerecommendations to proceed to a productive hydraulic fracture classmixture based on tornado diagrams utilizing the machine learning tomatch clusters of attributes of the hydraulic fracture classes thatcorrelate with a maximum production. The aforesaid system and methodgenerates recommended actions to control the hydraulic fracture classesor FracClasses 3 and 4 occurrences as a percentage of the hydraulicfracture class or FracClass 5 of perfect factures. In accordance with anexemplary embodiment of the claimed invention, the aforesaid system andmethod automatically updates the decision trees to estimate limits ofcombination of the down-hole pressure, the proppant concentration, theslurry volume and rate, and a sand volume and size based on trends inone or more historical hydraulic fracture successes and failures thatoccur in each well stage-by-stage, and automatically convey byself-driving, autopilot and/or other autonomous means directions offuture actions to the controller of hydraulic fracturing.

In accordance with an exemplary embodiment of the claimed invention, theaforesaid real-time processor stores the hydraulic fracture classes fromeach new well in the system integration database, thereby enablingsubsequent production of liquids, gas and water to be tested againststored hydraulic fracture class mixtures, real-time conditions, andperformance measurements as fractures unfold in real-time.

In accordance with an exemplary embodiment of the claimed invention, theaforesaid real-time processor generates one or more hydraulic fracturingconditions that minimizes ideal hydraulic fracturing conditionscomprised by at least reducing costs of a service company's time andenergy. The aforesaid system and method determines a proppant and waterconsumption and recommends a decision to proceed or stop said eachhydraulic fracturing stage because cost exceeds benefit.

In accordance with an exemplary embodiment of the claimed invention, theaforesaid real-time processor comprises a memory to storecomputer-executable instructions. The aforesaid real-time processor iscoupled to at least one transmitter to communicate with the hydraulicfracturing control center via a bi-directional messaging interface. Theaforesaid real-time processor executes the computer-executableinstructions to cause the hydraulic fracturing control center (or Fraccontrol center) to perform multiple actions. The hydraulic fracturingcontrol center receives recommendations from the Petroleum AnalyticsLearning Machine system. The Frac control center generates at least onerecommendation to increase production or cut costs of a well in progressby controlling a mix of the hydraulic fracturing class outcome usingdecision trees of the Petroleum Analytics Learning Machine system tomaximize an overall ell production. The Frac control center stores datafrom actions undertaken based on at least one recommendation in thesystem integration database to provide a feedback to the PetroleumAnalytics Learning Machine system about its recommendations based on thefuture production.

In accordance with an exemplary embodiment of the claimed invention, theaforesaid real-time processor computes a forecast for production of oil,natural gas, gas liquids, and water for a duration of a profitablehistory of a well, before delivery of the oil and gas to the surface.The aforesaid real-time processor continuously monitors and updates theproduction as the well ages. The aforesaid real-time processor providesan estimated ultimate recovery modification recommendations when adeviation from a forecasted, estimated ultimate recovery is predicted.

In accordance with an exemplary embodiment of the claimed invention, theaforesaid real-time processor analyzes a pipeline gathering system thatis monitoring data from maintenance and “pigging” (self directed orflowing cylinders of electronics that are pumped through the inside ofthe pipeline to make measurements of corrosion, fracturing, liquids andwater buildup, and other unsafe conditions within the pipeline) andstoring it in the system integration database. The monitoring datacomprises at least one of the following: time series of nodal pressure,liquids and gas compositions and volumes, maintenance records; and thePALM system identifies correlation clusters to predict optimal piggingschedules and looping directions for highest performance of a pipelinegathering system.

A composite tornado plot is then created for seasons, wet versus dry andhot versus cold. Forecasting of day-ahead and week-ahead pipelinegathering system capacity leads to the identification of maintenancethat will prevent the need to shut-in wells because of excessivegathering system capacity. Ranking by section of good to bad performingpipeline sections allows forecasting of susceptibility to liquidstrapping, actual versus planned pigging success, witches hat problemevents before they happen, and condensate restrictions needed to reduceactual/predicted production.

In accordance with an exemplary embodiment of the claimed invention, theMAP subsystem further comprises an Efficient Frontier Portfolioapplication to quantify outstanding cost/benefit that will then becalculated by the PALM system. Control is multi-objective; that is, itmust optimize a combination of capital cost, reliability, operationalcost, safety, as well as profitability, etc. The infrastructuremanagement has to accommodate market signals that are stochastic andother exogenous variables that are also stochastic such as weather andenvironmental concerns. The state space for control is large, buthandled by the PALM machine learning in order to provide optimal costbenefit control of the energy infrastructure of oil and gas fields.

Various other objects, advantages and features of the present inventionwill become readily apparent from the ensuing detailed description, andthe novel features will be particularly pointed out in the appendedclaims.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention is further explained in the description whichfollows with reference to the drawings, illustrating, by way ofnon-limiting examples, various embodiments of the invention, with likereference numerals representing similar parts throughout the severalviews, and wherein:

FIG. 1 is an illustration of a system overview of the Petroleum AnalyticLearning Machine (PALM) system in accordance with an exemplaryembodiment of the claimed invention;

FIG. 2 is an illustration of the schematic flow of the PALM system inaccordance with an exemplary embodiment of the claimed invention;

FIG. 3 is an illustration of the machine learning optimizer system andmachine learning tools common to all MAP subsystems of the PALM systemin accordance with an exemplary embodiment of the claimed invention;

FIG. 4 is a schematic illustration of various data from geology,geophysical, reservoir modeling, drilling, completions including thehydraulic fractures, production, pipeline gathering and exogenoussystems that are integrated into the System Integration Database, inaccordance with an exemplary embodiment of the claimed invention;

FIG. 5 is an illustration of the TotalVU dashboard that visualizes aMAPGEORES Tornado Diagram of Importance Weights in accordance with anexemplary embodiment of the claimed invention;

FIGS. 6A-B are illustrations of MAPFRAC automated classification ofhydraulic fracturing data in accordance with an exemplary embodiment ofthe claimed invention;

FIG. 7 is an illustration of a MAPFRAC result of intentionallyincreasing the FracClass 4 hydraulic fracture percentage per well in adrilling program in accordance with an exemplary embodiment of theclaimed invention;

FIG. 8 illustrates a Tornado Diagram of the Importance Weights ofMAPFRAC hydraulic fracture attributes that likely caused the productionimprovement in FIG. 7;

FIG. 9 illustrates a MACFRAC Decision Tree for arriving at a FracClass 4versus FracClass 5 result during the hydraulic fracturing of successivestages of a horizontal shale oil well in accordance with an exemplaryembodiment of the claimed invention;

FIG. 10 is illustration of the MAPPROD Tornado Diagram of ImportanceWeights that predict oil, gas and water production using all of theattributes available before production of first oil to the surface inaccordance with an exemplary embodiment of the claimed invention;

FIGS. 11A-D illustrate the MAPPROD predictions for oil, gas, and waterproduction in accordance with an exemplary embodiment of the claimedinvention; and

FIG. 12 is an illustration of the MAPGATHER optimizer for pipelinegathering systems performance improvement, including compressor stationmaintenance monitoring and “Pigging” scheduling of most needed flowpaths, in accordance with an exemplary embodiment of the claimedinvention.

DETAILED DESCRIPTION OF THE EMBODIMENTS

This application incorporates each of the following application byreference in its entirety: U.S. Pat. Nos. 6,826,483, 7,395,252,8,036,996 B2, and U.S. Pat. No. 8,560,476.

Turning to FIG. 1, there is illustrated a general overview of the systemincorporating the PALM 1000. In accordance with an exemplary embodimentof the PALM 1000 comprises a processor 1100, a machine analyticsproducts (MAP) 1200 subsystems, as well as a System Integration Database(SID) 1300, a Machine Learning Optimizer 1400, and a TotalVU controller1500 providing data visualization.

As shown in FIG. 2, in accordance with an exemplary embodiment, the PALMsystem 1000 feeds data to and archives analyses results from MAP 1200subsystems, including but not limited to: MAPGEORES 1210, MAPDRILL 1220,MAPFRAC 1230, MAPPROD 1240, MAPGATHER 1250 and MAPPORTFOLIO 1260. ThePALM Processor 1100 comprises a Machine Learning optimizer that predictsfuture results and prescribes actions to improve performance, andinteracts with the operator via the TotalVU controller 1500 and itsassociated user interface.

In accordance with an exemplary embodiment of the claimed invention, theMAPGEORES 1210 is a geologic, geophysical, rock properties, andreservoir modeling engine that scores the Importance Weights calculatedby the Machine Learning Optimizer 1400. Specifically, the predictor 1410and prescriptor 1420 of the Machine Learning Optimize 1400 uses anensemble of cluster and classification analyses in order to predictmaximum production before a well is produced to the surface.

In accordance with an exemplary embodiment of the claimed invention, theMAPDRILL 1220 is a real-time drilling data integration engine thatoptimizes drilling to match the designed pathway of the well includinghitting one or more landing zones, while minimizing sinuosity ofhorizontal and non-vertical components of the drilled well.

In accordance with an exemplary embodiment of the claimed invention, theMAPFRAC 1230 is a real-time hydraulic fracture classifier used tocontrol the class of hydraulic fractures (FracClass) stage-by-stage,onsite or off. MAPFRAC 1230 uses the FracClass classification system ofthe claimed invention to predict the optimal mixture of perfect fracturestages (not good for production if all stages of a horizontal laterallength are perfect, a surprise discovery of the claimed invention),versus the class of frac's that deliver late stage sand placement moreeffectively to the near wellbore. Inventors discovered that more than25% of these imperfect frac's out produced perfectly frac'ed wells inour reduction-to-practice example. Other FracClasses identified by thePALM system 1000 deal with the inevitable surface and wellboremechanical failures that occur in order to make decisions when toabandon a costly frac to minimize losses.

In accordance with an exemplary embodiment of the claimed invention, theMAPPROD 1240 is a production forecaster that convolves the actualattribute values of hundreds to thousands of attributes coming into thesystem from historical wells, as well as each new well as it progresses,to maximize production for all wells in a play. The result, ascontrolled by the actions recommended by the PALM 1100 processor, is theoptimization of the production of oil, natural gas, and natural gasliquids while minimizing water production (a cost) over time.

In accordance with an exemplary embodiment of the claimed invention, theMAPGATHER 1250 integrates the pipeline field data from gatheringpipelines and production facilities, a real-time system for optimizingmaintenance and pigging schedules, while minimizing liquids dropout inorder to maximize fluid and gas throughput of the pipeline gatheringsystem.

In accordance with an exemplary embodiment of the claimed invention, theMAPPORTFOLIO 1260 manages the efficient frontier of costs versusbenefits for each well, field, play or company, and the MAP ETC. 1270 isa subsystem or an application engine specifically built to address aparticular situation or customized for a specific customer's need orrequirement.

Turning now to FIG. 3, in accordance with an exemplary embodiment, thereare listed machine analytics algorithms and tools commonly accessible toall MAP 1200 subsystems within the PALM 1000. The Machine LearningOptimizer 1400 computes adaptive stochastic control, locally sensitivehashing, and MapReduce parallelization in Hadoop. Unstructured Analyses1401 extract, retrieve and mine information from text, perform entityand pattern recognition, log-rank, perform keyword extraction, semanticanalysis, knowledge discovery, sentiment analysis and noisy textprocessing. A Clustering Predictor 1410 computes K-means, K-medoids,region growing, and non-parametric modeling. A Regression Predictor 1411computes linear and support vector regression, and classification andregression trees (CART). A Feature Selector 1412 computes and ranksImportance Weights, Chi-square goodness of fit, Fischer scoreprobabilities, principal component analysis (PCA), and contains variouswrapper methods. An Ensemble Prescriptor 1420 computes bagging, mountainclimbing optimization, boosting of aggregate classifiers, random forestdecision trees, and gradient boosters. A Classification Prescriptor 1421computes logistic regression, support vector machines, K-Nearestneighbor, Decision tree modeling, and Neural networks and Deep learning.A Time Series Prescriptor 1422 computes Multivarieant time series,Hidden Markov models, and non-parametric Bayesian models.

In accordance with an exemplary embodiment of the claimed invention, asshown in FIG. 4, the system integration database (SID) 1300 is thecentral data repository for all data sources. The SID is amulti-architectural data center that incorporates components ofdifferent database technologies. One component is based on relationaldatabase management system (DBMS), which is for the traditionalstructured column based data management. The SID 1300 also features aNoSQL data management, which provides a mechanism for storage andretrieval of data not only in tabular relations. For example, textualdata, such as PDFs, image data such as frac's, audio and video data canbe analyzed via the NoSQL architecture for storage, and efficientretrieval. An example NoSQL database is MongoDB. Another component ofthe SID 1300 is a distributed file system. In the petroleum industry,terabytes of data are generated every day, such as time series hydraulicfracture data, well log and measurement-while-drilling data, and sensordata that monitors production and delivery to processing plants. How tostore these data, and make use of such large-scale data poses achallenge in this domain. A distributed file system facilitates thestorage and maintenance of the data, and provides efficient datacomputations and analytics. For example, Hadoop is a framework thatallows for the distributed storage of data and distributed processing oflarge data sets across clusters of computing resources. A component ofthe SID 1300 makes use of Hadoop distributed file system (HDFS) for datastorage, and MapReduce techniques for further data learning andcomputation. The large-scale data analytics in oil and gas benefit fromthe recent development of big-data technologies. Hadoop ecosystem is aframework that is based on the MapReduce algorithm for big dataanalytics. Hadoop distributed file system (HDFS) stores large-scale ofdata in a distributed network across computing clusters. Datacomputation is performed on each computing node in the Map step, and anintermediate output is combined to perform a global computation in theReduce step. Among many components in the Hadoop Echosystem that can beapplied in the oil and gas domain, Apache Hive is a data warehouseinfrastructure built on top of Hadoop for providing data summarization,query, and analysis. The Apache Mahout provides an environment forquickly creating scalable machine learning applications.

Within the SID 1300, in accordance with an exemplary embodiment of theclaimed invention, geology and geophysical data 1310 include 2D, 3D & 4Dseismic data and interpretations such as the location and form offaults, anticlines, synclines, fractures, stratigraphic features,integrated well logs and areal maps. Rock property data include landingzone targets, target interval, target height, thickness of sequences,landing sequence type, gas shows, core analyses, mudlogs. Well log andmeasurement-while-drilling log analysis are included, such asstructures, thickness, formation identification, normalized curve data,gamma ray, effective porosity, density, resistivity, TOC (total organiccarbon), water saturation, and gas in place data. Reservoir modelinginputs and outputs are included.

Within the SID 1300, in accordance with an exemplary embodiment of theclaimed invention, drilling data 1320 include surveys such as MD(measured depth), inclination, azimuth, TVD (total vertical depth), VS(vertical steering), departure north south east west, DLS (dog legseverity), build, turn, parameters, such as WOB, ROP, torque,circulation rate, gamma ray, differential PSI, choke position, hookload, flow, alarm states, pump rates, pump stokes, build rate, blockheight, tank volumes, over pull, northing, easting, inclination,azimuth, rotary torque, trip speed, tank fill, walk rate, resistivity,rotary RPM, mud viscosity, mud weight, 3rd party gas, deviation fromplan, formation density, and wellbore schematics, such as conductorcasing depth, water casing depth, minimum casing depth, surface casingdepth, production casing depth, float subs, float collars, float shoes,marker joints, cement design, displacement volume, additives type, andadditives volume data.

Within the SID 1300, in accordance with an exemplary embodiment of theclaimed invention, completions data 1330 include structured digital datasuch as fracture treatment, such as number of stages, landing zone foreach fracture stage, fracture gradient, breakdown pressure, breakdownrate, min/max treating rates, min/max treating PSI (pounds per squareInch), ISIP (instantaneous shut-in pressure), stage phases, such asstart/end date & time, fluid type, proppant density, slurry volume,cumulative slurry volume, clean volume, cumulative clean volume,proppant volume, start/end rates, start/end pressures, additive type,additive name, additive volume, and perforations, such as stage number,top perforation, bottom perforation, TVD (total vertical depth) ofperforation, shot density SPF (shots per foot), shots planned, actualnumber of shots, cluster size, perforation diameter, phasing, chargesize, penetration depth, gun size, charge type data. Unstructuredtextual data that the SID 1300 can incorporate includes mechanical toolinformation, well completion logs and schematics, lists of toolconfigurations put into wells for completion and production, salesorders with part numbers, technical limits of the tool string, and joblogs (such as operator, data/time, activity, remarks, job number, soldto, billed to, plant, Purchase Order/Authorization For Expenditurenumber, shipped to, description, address, details, well Identifier,etc.).

Within the SID 1300, in accordance with an exemplary embodiment of theclaimed invention, production data 1340 include gas analysis, such asBTU calculation, depletion (Z) factor, sample pressure, sampletemperature, molar component percent, GPM (gallons per minute) measure,production estimates, such as daily gas, daily condensate, daily water,daily casing pressure, daily tub pressure, daily pad volume, condensatehaul tickets, water haul tickets, tank gauges—top, tank gauges—bottom,and SCADA (supervisory control and data acquisition), such as gas rate,differential pressure, tubing pressure, casing pressure, ESD (emergencyshutdown) alarms, separator pressures, choke position, LEL (lowerexplosive limit) readings, condensate density, water density, tankgauges—top, tank gauges—bottom, EBU Data, flash separation data, VRU(vapor recovery unit) data, battery voltage data.

Within the SID 1300, in accordance with an exemplary embodiment of theclaimed invention, pipeline gathering data 1350 includes location, pipesize, topographical height, and size configuration, fluid and gascomposition, and pigging history, as well as maintenance schedules,type, time, place, and result of all previous incidence reports andrepair records by pipeline section and GPS location, compressor stationand equipment, pigging data acquisition, liquids trapped by location andtime, and all other relevant remotely and locally gathered operationalSCADA data.

Within the SID 1300, in accordance with an exemplary embodiment of theclaimed invention, exogenous data 1360 include primarily weather historyand future forecasts.

In accordance with an exemplary embodiment of the claimed invention, theMAPGEORES 1210 computes production forecasts entirely from geological,geophysical, rock property and reservoir simulation data known beforethe well is spudded. The tornado diagram of importance weightscalculated by MAPGEORES 1210 as exemplary displayed by the TotalVU 1500is shown in FIG. 5. The calculated importance weights are used by thePALM 1000 to predict production accuracy in accordance with an exemplaryembodiment of the claimed invention. Appendix 1 is a list of attributesshown in FIG. 5 ranked by their importance weights calculated byMAPGEORES 1210 using Support Vector Regression. The prediction ofproduction of oil, natural gas and water when Importance Weights wereconvolved with the same labeled attributes specific to each well werefound to be 67% accurate using this initial set of geology, geophysics,rock properties, and reservoir modeling attributes.

The MAPGEORES 1210 utilizes machine learning of the historicalstructured data to compute Importance Weights for the attributes thatrepresent all the data available before spud. The machine learningalgorithms of the MAPGEORES 1210 uniquely combine the parameters ofsupport vector and linear regression, allowing the construction of theTornado diagrams, as exemplary shown in FIG. 5, to represent theImportance Weights of each attribute that correlates with a positiveproduction prediction result (the bars to the right) and the importanceof negative weights of each attribute that correlates with a positiveproduction prediction result (the bars to the left). The predictedproduction is then compared to the actual production to derive anaccuracy score. The future production accuracy is approximately 67% forthe reduction-to practice shown in FIG. 5, whereas a random forecastingwould be accurate only 50% of the time.

In accordance with an exemplary embodiment of the claimed invention, theMAPGEORES 1210 assembles a wide array of unstructured textual and imagedata (such as .pdf) to create additional attributes that are included inthe machine learned ranking of Importance Weights, forming newattributes such as exemplary shown in Table 1.

TABLE 1 Procedure for Progressive Clustering with Learned Seeds toCompute new Machine Learning Attributes 1. Tree path extraction 2. Pathscoring 3. Class contribution calculation 4. Seed points retrieval 5.K-Means clustering using retrieved seeds

In accordance with an exemplary embodiment of the claimed invention, theMAPDRILL 1220 is a real-time synthesizer of the data coming into the SID1300 during the drilling process, which can be 2000 or more data pointseach second. The MAPDRILL 1220 optimizes the drilling to match asclosely as possible the designed pathway of the well including hittingone or more landing zones, while minimizing sinuosity of horizontal andnon vertical components of the drilled well. In accordance with anexemplary embodiment of the claimed invention, the MAPDRILL 1220minimizes the sinuosity of the horizontal component during the drillingof wells by monitoring and prescribing latitude, longitude and depthmodifications to the inertial navigation steering mechanism. The largerthe amplitude of the sinuosity of the horizontal well, or how much itdeviates from the planned target path of the well, the more chances forliquids to pool in the valleys of the wellbore, which often can blockthe path of the liquids and gases to the surface. In accordance with anaspect of the claimed invention, the drilling console of a modernhorizontal drilling rig receives data transmitted in near real-time fromdownhole, thereby allowing the driller to steer the horizontal well toprevent it from sinusoidal spiraling which can cause oil to havedifficulty drilling to the surface.

In accordance with an exemplary embodiment of the claimed invention, theautomated classification of hydraulic fracturing data by the MAPFRACclassifier 1230 to isolate a FracClass 4 hydraulic fracture, asillustrated in FIG. 6A, that is struggling to inject the last of itsheaviest proppant, compared to a more “perfect” FracClass 5 frac thatdid not inject enough of the heaviest sand and proppant to cause thelate pressure rise, as illustrated in FIG. 6B. The inventors discoveredthat a mix of FracClass 4 and 5 is required to produce a most productivewell.

MAPFRAC classifier 1230 utilizes machine learning methods to classifythe wells to be those with highest production versus lowest production.Attributes for machine learning include data sources in addition togeology, geophysics, rock properties, reservoir simulation, such aslanding zones, stress gradients and other hydraulic fracturingattributes we invented such as FracClass completion classes. The totaloil, gas, condensate, and water production, and their normalizedproduction by flow days, normalized for perforated lateral length, areused as response variables. Classification methods such as logisticregression, naïve Bayes, support vector machine, decision trees (e.g.CART, ID3, C4.5, CHAID), k-nearest neighbors, neural networks and deeplearning networks are used by the MAPFRAC classifier 1230. Predictionaccuracy, precision, and recall for each class are metrics used by thePALM 1000 to evaluate the production forecasting performance. Regressionmodels such as linear regression, support vector regression,classification and regression trees (CART) can be also used by theMAPFRAC classifier 1230. R-Square, mean square error, among others, canbe used to evaluate the regression performance. If a ranking isgenerated by the MAPFRAC classifier 1230 where the top of the rank listare high producing wells, and the bottom are low producing wells,receiver operating characteristic (ROC) curves and area under the ROCcurve (AUC) are used to evaluate the ranking performance.

In accordance with an exemplary embodiment of the claimed invention, theensemble methods that combine multiple classifiers can be used by thePALM 1000 to improve the overall robustness and reliability of themodel. These ensemble methods include Ada boost, random forest, gradientboosting machine, and other bagging, and boosting techniques. TheMAPFRAC classifier 1230 executes a unique automated time seriesclassification schema using machine learning feature recognition todevelop clusters of hydraulic fracture classes unique to the claimedinvention, and then correlates the abundance of stages of each class tohighest production of each well, as shown in Table 2.

TABLE 2 MAPFRAC Machine Learning Steps for FracClass AutomatedClassification of Hydraulic Fractures as illustrated in FIG. 7 (1231)Automatically select time of the beginning of sand change to heaviestsand proppant = timeheavysandstart. (1232) Automatically select pressurepercentile at beginning of heaviest sand density increase = %pressureheavysandstart. (1233) Automatically select time of the densitydrop at the end of the heaviest sand injection =timedensitydropheavysandend. (1234) Automatically select pressurepercentile at the time of the end of the heaviest sand injection = %pressuredropheavysandend. (1235) Automatically calculate the slope ofthe linear regression of the pressure from beginning of heaviest sandinjection to the end of the heaviest sand injection =slopepressureheavysand. (1236) Automatically calculate the intercept ofthe linear regression of the pressure at the end pressure of theheaviest sand injection = Interceptpressureheavysand. (1237)Automatically assign a FracClass for each Hydraulic Fracture based uponwhether the Slope (1235) at the Intercept (1236) is positive, wherewiththe Hydraulic Fracture is assigned a classification of 4, representing astruggle to inject the last of the heaviest sand into the rock formation= FracClass 4, or (1238) Automatically assign a FracClass for eachHydraulic Fracture based upon whether the Slope (1235) at the Intercept(1236) is zero to negative, wherewith the Hydraulic Fracture is assigneda classification of 5, representing no struggle to insert the last ofthe heaviest sand into the formation = FracClass 5. (1239) Automaticallycalculate a statistical root mean squared scatter of the linearregression of the pressure from the beginning of heaviest sand injectionto the highest pressure = rsquared.

The claimed invention has solved the problem of not knowing whatproduction comes from which hydraulic fracture, stage-by-stage, byautomating a classification scheme that the MACFRAC classifier 1230correlates with high versus low production using at least 150 historicalwells and at least 2000 hydraulic fracture stages per play in shale oiland gas basins around the world. FracClass 1 in the claimedclassification schema is an incomplete fracture attempt that must beremoved from the analysis dataset. FracClass 2 fracs were either“Emergency Shut Downs” (ESD) because of surface equipment failures, fracjobs cut short for any surface reason such has lightning and badweather, or equipment shutdown (SD) that resulted in a full job but nota successful frac. FracClass 3 fracs were successful, but only afterre-perforations that were required by the sand sweep resulting in thewhole wellbore being packed off with sand. The most successful FracClass4 fracs occurred when more that one quarter of the stages in ahorizontal well resulted in late injection pressure rises at the nearwellbore due to struggles to place the full allotment of late sandproppant.

A majority of FracClass 4 fracs correlated with subsequent high wellproduction, surprisingly. FracClass 4 fracs can be independentlyidentified within the completions data by the real-time processor, thecompletions data comprising time series hydraulic fracture dataincluding surface and downhole pressures, slurry compositions and watermixes, sand volumes and proppant weights, breakdown pressure, proppantconcentrations and shut-in pressure for each hydraulic fracture. A timeof a density drop that ends a first sand injection 1231 is one of thekey attributes. A pressure percentile at the time of the first densitydrop 1232 is also one of the key attributes. A time of a density dropthat ends a second sand injection with sand larger in diameter andheavier than the sand used in the first sand injection is one of the keyattributes 1233. A pressure percentile at the time of the second densitydrop is one of key attributes 1234. A slope in the time of a pressuredrop at an end of shut-in is one of the key attributes. Automaticcalculation of the slope of the linear regression of the pressure frombeginning of heaviest sand injection to the end of the heaviest sandinjection at the end pressure of the heaviest sand injection 1235, andcomparison to the end of the proppant injection 1236 ends the slope fit.Automatic assignment of a FracClass for each Hydraulic Fracture is basedupon whether the Slope at the Intercept is positive 1237, wherewith theHydraulic Fracture is assigned a classification of FracClass 4,representing a struggle to inject the last of the heaviest sand into therock formation, or the Slope at the Intercept 1238 is zero to negative,wherewith the Hydraulic Fracture is assigned a classification ofFracClass 5, representing no struggle to insert the last of the heaviestsand into the formation.

That is, a time of a beginning of a sand change from a lighter to theheaviest sand is one of the key attributes. A pressure percentile atbeginning of a heaviest sand density increase is one of the keyattributes. A time of a highest pressure after the sand change to theheaviest sand is one of the key attributes. A pressure percentile of amaximum heaviest sand change is one of the key attributes. A slope of alinear regression of a pressure from beginning to end of the heaviestsand injection is one of the key attributes. An intercept of the linearregression of pressure from the beginning of the heaviest sand injectionto the highest pressure at the end of the heaviest sand injection is oneof the key attributes. The measure of scatter of the linear regressionof the pressure from the beginning of the heaviest sand injection to thehighest pressure at the end of the heaviest sand injection from stage tostage is another of the key attributes.

The MAPFRAC classifier 1230 discovered that horizontal shale oil and gaswells with more than 75% “textbook perfect” FracClass 5 hydraulicfracture stages produce less oil and gas than wells with less than 75%of FracClass 5 fracs and more abundance of FracClass 3 and 4 hydraulicfracture stages produce more oil and gas. The MAPFRAC result ofintentionally increasing the FracClass 4 hydraulic fracture percentageper well in a drilling program in 2013 versus the preponderance of more“perfect” FracClass 5 wells from the 2009-2012 drilling program isexemplary shown in FIG. 7. In fact, the PALM 1000 predicted up to 320%improvement in oil production by increasing the FracClass 4 hydraulicfractures. This discovery was made in wells completed from 2009 through2012, as shown in FIG. 7, where there was an improvement from the highcost perfect FracClass 5 dominated wells (diamonds of group 1 inset), tothe medium to low cost FracClass 3 and 4 dominated wells (circles andtriangles of groups 2 and 3 respectively). The high cost label for Group1 diamond wells was because the hydraulic fractures cost the same, butthe production benefit was diminished when compared to the Group 2medium cost circle wells and low cost triangle wells. Average productionfor the 29 high cost wells was 2600 bbl/day, but 3500 bbl/day for 39medium cost wells and 3600 bbl/day for 28 low cost wells, as theyproduced from 2009-2012. In 2013, a concerted effort was made to drill ablind test of wells dominated by FracClass 3 and 4 hydraulic fracturestages (squares). Twenty wells produced an average of 6150 bbl/day, fora production performance improvement over the “perfect” wells of 225%.

FIG. 8 illustrates a Tornado Diagram of the Importance Weights ofMAPFRAC hydraulic fracture attributes that likely caused improvement inproduction for 2013 drill program shown in FIG. 7. Appendix 2 is aglossary of the top 20 attributes in FIG. 8. In accordance with anexemplary embodiment of the claimed invention, the MAPFRAC classifier1230 identified improvements in production in 2013 by ImportanceWeights; better drilling into the targeted, deeper landing zone, alarger number of hydraulic fracture stages, longer perforated laterallength of the horizontal wells, and more total sand proppant injectedinto the formation during hydraulic fracturing, in that order. TheFracClass 4 (the more the better) and FracClass 5 (the fewer the better)were ranked 13th and 14th most Important Weights, indicating they aredependent variables to the higher ranked independent variables listedabove plus shorter perforation cluster and hydraulic fracture spacingused in 2013.

In accordance with an exemplary embodiment of the claimed invention, asillustrated in FIG. 9, the steering of hydraulic fractures to the moreadvantageous FracClass 4 fracs can be controlled from the “Frac ControlCenter” in real time using Random Forrest decision trees calculated bythe MAPFRAC classifier 1230, which recomputes the “yes/no, if/then”branching of the tree every few seconds during each new frac. The boxedpaths in FIG. 9 predict how to make a FracClass 4 instead of a FracClass5 dominated oil well mix, which produced a much higher volumes of oil inFIG. 8.

As each hydraulic fracture proceeds from light sand to heaviest sandproppant, first the slope of the pressure is monitored. SuccessfulFracClass 4 fracs can be obtained whether the slope is equal to or lessthan 0.15, in which the left branches (1249, 1247) of the Decision Treebecome critical, or the slope is greater than 0.15, in which the rightbranches are critical. If the frac follows the rightmost branches (1248)of FIG. 9, the percentage pressure drop during injection of the heaviestsand proppant then becomes critical. If the percentage is less than 48%,the tree branches to the left (1247), and maximum pressure of the heavysand proppant injection must be kept less than 98%. A FracClass 4 willthen have a 90% success rate. If the pressure increases to more than100% of what it has been, then the odds for a FracClass 5 frac aregreater than 80%. If the percentage pressure drop is greater than 48%however, the rightmost decision tree branches will be followed. Then theslope of the pressure of heaviest sand proppant injection must bemaintained at <=40% in order to have an 80% chance of developing aFracClass 4 frac.

If the slope of the initial pressure of the heaviest sand proppant isless than 0.15, then there is a 2;1 chance that the leftmost branches(1249) in FIG. 9 will be followed. Then the percent pressure drop of theheaviest sand proppant injection must be kept higher than 56% in orderto be certain of a FracClass 4 frac.

FIG. 10 is illustration of the MAPPROD Tornado Diagram of ImportanceWeights that predict oil, gas and water production using all of theattributes available before production of first oil to the surface inaccordance with an exemplary embodiment of the claimed invention. TheMADPPROD optimizer 1240 used 114 attributes available from the SID 1300,which achieved a production accuracy of 97% using historical data.Appendix 3 is a glossary of the 184 structured attributes and many moreunstructured attributes used to select the 114 most important attributesfor predicting production.

In accordance with an exemplary embodiment of the claimed invention, theMAPPROD optimizer 1240 uses a Machine Learning optimizer to compute theImportance Weights for the hundreds of multi-dimensional attributes thatrepresent all the data available at each time as the well proceeds, frombefore spud, to after drilling and finally after completion. Inaccordance with an aspect of the claimed invention, Table 3 illustratesthe Importance Weights of the 114 attributes in the reduction topractice study, combining the data common to all analyzed wells from thesystem integration database 1300, which contains 185 digitallystructured attributes and numerous unstructured textual attributesdefined in the glossary of Appendix 3. FIG. 10 illustrates the top 20Importance Weights of Table 3. In accordance with an exemplaryembodiment of the claimed invention, FIGS. 11A-C illustrate theImportance Weights of the 114 attributes, when convolved with thespecific attributes of each well contributed to a Production Predictionfor Oil, Gas, and Water of 97%+/−2.7%.

TABLE 3 The Importance Weights of the 114 attributes in the reduction topractice study. Importance Rank Attribute Weight 1 Landing Zone MajorityPct 2.77 2 Number of Stages 2.69 3 Reservoir Modeling Equation −2.35 4FracLookback_Perforated Lateral Length 2.35 5ReservoirModelingData_Linear Flow Parameter −2.27 6 FracLookback_TotalSand Per Well 2.11 7 FracLookback_BBLS Per ft 2.05 8RockPropertiesAvg_Permeability 1.95 9 FracLookback_Res Model CorrectionFactor 1.95 10 FracLookback_Cluster Spacing −1.83 11FracLookback_Breakdown Pressure/ISIP −1.75 12 FracLookback_BTU −1.68 13FracLookback_Breakdown Pressure −1.55 14 ReservoirModelingData_FractureSpacing −1.51 15 FracLookback_UpDip/DownDip 1.48 16 FracLookback_ISIPInstantaneous Shut In Pressure 1.48 17 StressGradientAvgByWell_MWD_GammaRay 1.47 18 FracLookback_Sand Lbs Per Ft 1.44 19 FracLookback_InitialProduction 1.44 20 StressGradientAvgByWell_Youngs Modulus −1.43 21RockPropertiesAvg_Porosity 1.43 22 FracLookback_Horizontal Well Azimuth1.42 23 LandingPointFeatures_Landing Zone Std 1.35 24RockPropertiesAvg_Temp_Max −1.33 25 LandingPointFeatures_Landing ZoneMajority 1.28 26 LandingPointFeatures_Landing Zone Average 1.27 27RockPropertiesAvg_Gas In Place −1.23 28 ReservoirModelingData_ScalingFactor −1.21 29 ReservoirModelingDataArea Stimulated/ −1.15 ReservoirVol 30 FracLookback_Frac Gradient 1.14 31 FracLookback_InitialProduction Per Cluster 1.13 32 StressGradientAvgByWell_Base MeasuredDepth 1.08 33 StressGradientAvgByWell_Measured Depth 1.08 34StressGradientAvgByWell_Top Measured Depth 1.07 35FracClassFeatures_FracClass Std 0.99 36 FracClassFeatures_Has FracClass4 Majority 0.98 37 FracLookback_Has FracClass 5 Majority −0.95 38RockPropertiesAvg_Water Saturation −0.90 39 ReservoirModelingData_NetPay Thickness −0.90 40 RockPropertiesAvg_Total Organic Carbon 0.86 41RockPropertiesAvg_Density −0.84 42 RockPropertiesAvg_BTU 0.84 43LandingPointFeatures_Is In Landing Zone 1 −0.81 44 FracLookback_AvgRate0.81 45 RockPropertiesAvg_Pressure 0.79 46 ReservoirModelingData_Numberof Stages 0.77 47 RockPropertiesAvg_Vitronite Reflectance 0.75 48RockPropertiesAvg_Gamma Ray −0.70 49 ReservoirModelingData_Skin −0.69 50RockPropertiesAvg_Porosity Log −0.66 51 ReservoirModelingData_InitialGas Saturation −0.66 52 RockPropertiesAvg_Longitiude −0.66 53StressGradientAvgByWell_Longitude −0.65 54 LandingPointFeatures_Is InLanding Zone II_b 0.58 55 FracClassFeatures_FracClass 5 Pct −0.57 56FracClassFeatures_FracClass 4 Pct 0.57 57 RockPropertiesAvg_YoungsModulus 0.57 58 ReservoirModelingData_Reservoir Temperature −0.48 59FracLookback_Small Proppant Design −0.47 60 LandingPointFeatures_IsInLanding Zone III_a 0.47 61 FracClassFeatures_FracClass 4 And Above Pct0.45 62 FracLookback_Avg Pressure −0.44 63 LandingPointFeatures_Is InLanding Zone III_b −0.42 64 FracLookback_Breakdown Rate 0.41 65RockPropertiesAvg_Avg Porosity −0.41 66 LandingPointFeatures_Is InLanding Zone II_a 0.38 67 StressGradientAvgByWell_Total 0.38 VerticalDepth/_Perf 68 RockPropertiesAvg_Depth −0.37 69StressGradientAvgByWell_Depth −0.36 70 FracLookback_Max Proppant Conc0.35 71 FracLookback_BTU Attribute −0.35 72 RockPropertiesAvg_MeasuredDepth 0.31 73 RockPropertiesAvg_TVD Horizontal −0.30 74StressGradientAvgByWell_Poisons Ratio 0.30 75FracClassFeatures_FracClass Average −0.29 76 FracClassFeatures_FracClass3 Pct −0.26 77 RockPropertiesAvg_Vitronite_Reflectance by Zone −0.26 78FracLookback_Clean Vol 0.25 79 FracLookback_Slurry Vol 0.24 80FracClassFeatures_Has FracClass 2 0.24 81 LandingPointFeatures_Is InLanding Zone II_c 0.24 82 LandingPointFeatures_Is In Landing Zone I−0.22 83 ReservoirModelingData_Corerection Factor −0.22 84ReservoirModelingData_EffectivePorosity −0.22 85 LandingPointFeatures_IsIn Landing Zone I_c 0.21 86 FracLookback_Max Slurry Rate −0.19 87FracLookback_Water Lbs Per BBL 0.19 88 FracLookback_Large ProppantDesign 0.18 89 ReservoirModelingData_Fracture Conductivity 0.18 90FracLookback_Avg Sand Per Stage 0.16 91 FracLookback_Volume 100 Mesh−0.14 92 FracClassFeatures_FracClass Majority −0.12 93ReservoirModelingData_Matrix Permeability −0.11 94RockPropertiesAvg_Landing Zone Thickness 0.09 95FracClassFeatures_FracClass 2 Pct −0.09 96 FracClassFeatures_FracClass 3Pct 0.08 97 FracLookback_Volume Sand 30/50 0.07 98 FracLookback_FluidDesign −0.06 99 FracClassFeatures_Frac Class Majority Pct −0.06 100FracClassFeatures_Has FracClass 4 0.03 101 RockPropertiesAvg_Latitude−0.02 102 FracLookback_Max Pressure −0.02 103StressGradientAvgByWell_Latitude −0.02 104StressGradientAvgByWell_Stress Grad 0.01 105RockPropertiesAvg_Porosity/Resistivity −0.01 106ReservoirModelingData_Perforated Lateral Length 0.00 107LandingPointFeatures_Is In Landing Zone I 0.00 108LandingPointFeatures_Is In Landing Zone III_c 0.00 109LandingPointFeatures_Is In Landing Zone II_c 0.00 110LandingPointFeatures_Is In Landing Zone I_c 0.00 111LandingPointFeatures_Is In Landing Zone I_b 0.00 112LandingPointFeatures_Is In Landing Zone I_a 0.00 113LandingPointFeatures_Is In Landing Zone IV 0.00 114 FracLookback_FluidPct Design 0.00

In accordance with an exemplary embodiment of the claimed invention, theMAPPROD optimizer 1240 convolves the Importance Weights for all wells ineach study area f with g which is each attribute value specific to thewell for which future production of oil, gas and water is beingcalculated, wherein f*g is an integral transform of the product of thetwo functions as attributes specific to that well under study. Theintegral transform then predicts the future production of the well understudy before the oil and gas are delivered to the surface and usesfuture production to calculate an accuracy of that initial forecast.

FIGS. 11A-D illustrate the MAPPROD predictions for oil, gas, and waterproduction using the dataset of 0 Root Mean Square accuracy varied from+/−2.7% for 114 attributes to +/−11% for 32 attributes. In accordancewith an exemplary embodiment of the claimed invention, as shown in FIGS.11A-C, the MAPPROD optimizer 1240 convolved the Importance weights usingthe 114 attributes in Table 3 with the 156 wells in the reduction topractice dataset, resulting in production predictions for oil, gas andwater that were found to be 97% accurate compared to the initialforecast. As shown in FIG. 11D, the Root Mean Square Error (RMSE) of thepredicted versus actual production forecasts increased from +/−11% for32 MAPGEORES attributes, to 9% for 45 MAPGEORES plus MAPDRILLattributes, to 7% for 62 MAPFRAC added attributes, to 6% when FracClassattributes were added, and finally to 2.7% when all resulting attributesthat were available before first oil was produced to the surface.

As exemplary shown in FIG. 12, the pipeline gathering system 1600 iscritical to deliver the production to market, the MAPGATHER analyticengine 1250 correlates cause-and-effect events between producing wells,production pads and pipeline gathering and compression station events1251 that might be mitigated by preventive maintenance, day-aheadforecasts of available gathering system capacity, and changes to loopsthat may be created within the gathering system to alleviate congestionand prevent choke points. This information can be conveyed automaticallyby self-driving, autopilot and/or other autonomous means to thecontroller for management of the pipeline gathering system.

For compressor stations 1251 within the pipeline gathering system 1600,the MACGATHER analytic engine 1250 continuously analyzes clusters ofcorrelation in compressors, engines, and separator performances, andprescribes maintenance routines that need to be changed. In accordancewith an exemplary embodiment of the claimed invention, the MAPGATHERanalytic engine 1250 provides an analytical solution that analyticallyanalyzes the effects of weather on incidence reports, day and nightscheduling, inspections, etc. and automatically conveys this informationby self-driving, autopilot and/or other autonomous means to thecontroller for management of the pipeline gathering system. TheMAPGATHER analytic engine 1250 generates a composite Tornado plot forseasons, wet versus dry and hot versus cold. Forecasting of day-aheadand week-ahead pipeline gathering system capacity by the MAPGATHERsubystem 1250 leads to the identification of maintenance that willprevent the need to shut-in wells because of excessive gathering systemcapacity. The MAPGATHER analytic engine 1250 ranks section by section ofgood to bad performing pipeline sections (by section) allows forecastingof susceptibility to liquids trapping, actual versus planned piggingsuccess, witches hat problem events before they happen, and condensaterestrictions needed to reduce actual/predicted production.

As exemplary shown in FIG. 12, the pipeline gathering system 1600 maynot be pigging optimally. In accordance with an exemplary embodiment ofthe claimed invention, the MAPGATHER analytic engine 1250 predicts, andthen prescribes better pigging schedules. For example, the MAPGATHERanalytic engine 1250, detects predictable pressures that build uprepeatedly at specific locations 1252 because of too much liquidsaccumulation in topographic low points that are prescribed for higherlevels of Pigging surveillance. This information is automaticallyconveyed by self-driving, autopilot and/or other autonomous means to thecontroller for management of the pipeline pigging system.

In general, various omissions, modifications, substitutions and changesin the forms and details of the device illustrated and in its operationcan be made by those skilled in the art without departing in any wayfrom the spirit of the present invention. Accordingly, the scope of theinvention is not limited to the foregoing specification, but instead isgiven by the appended claims along with their full range of equivalents.

APPENDIX 1 List of Attributes in FIG. 5, Ranked by their ImportanceWeights Calculated by the MAPGEORES Subsystem

-   -   1. Permeability    -   2. Average Pressure    -   3. Log Porosity    -   4. Linear Flow Parameter    -   5. Reservoir Modeling Equation    -   6. Effective Porosity    -   7. Measured Depth    -   8. Perforated Lateral Length    -   9. Total Vertical Depth    -   10. Poissons Ratio    -   11. Total Organic Carbon    -   12. British Thermal Units    -   13. Reservoir Volume    -   14. Average Depth    -   15. Average Thickness    -   16. Number of Stages    -   17. Vitrinite Reflectance    -   18. Perforation Length    -   19. Water Saturation

APPENDIX 2 Glossary of Top 20 Attributes in FIG. 7, Ranked by theirImportance Weights Calculated by the MAPPROD Optimizer

-   1. Landing Zone Majority Pct=Highest percentage geological formation    that the majority of the horizontal portion of the well landed in.-   2. Number of Stages=The number of Hydraulic Fracture stages within    the perforated lateral length of the horizontal portion of the    wells.-   3. Reservoir Modeling Equation=The fluid flow equation used by the    Reservoir Simulator.-   4. Perforated Lateral Length=Total length of the horizontal portion    of the well that was perforated.-   5. Linear Flow Parameter=Reservoir simulator estimate of the linear    flow parameter.-   6. Total Sand Per Well=Total sand proppant injected into the    formation for each Hydraulic Fracture by stage.-   7. Barrels of Proppant/Slurry pumped per foot=per Hydraulic Fracture    stage.-   8. Rock Properties Avg Perm=Average Permeability of the formation    estimated from logs or measured from sidewall cores.-   9. Reservoir Model Magic Correction Factor=Also known as MagicFR, is    a linear correction to the Fluid Flow equation to scale it to the    actual formation production performance.-   10. Fracture Cluster Spacing=of all Hydraulic Fracture stages per    well.-   11. Breakdown Pressure to ISIP Ratio=Hydraulic Fracture breakdown    pressure divided by Instantaneous Shut In Pressure after all    treatments are completed.-   12. BTU Content of Formation=British Thermal Units of combustible    power of the hydrocarbons in the formation.-   13. Fracture Breakdown Pressure=Pressure at which the Hydraulic    Fracture was initiated in the formation.-   14. Fracture Spacing=Estimated natural fracture spacing used by the    Reservoir simulator.-   15. Up Dip to Down Dip Ratio=of a sinuous horizontal portion of the    well.-   16. Fracture ISIP=Instantaneous Shut In Pressure of the Hydraulic    Fracture at the end of the treatment as pressure is ramped down.-   17. Avg By Well MWD Gamma Ray=Measurement While Drilling average of    the Gamma Ray content of the formation in the horizontal portion of    the well.-   18. Proppant/Slurry Lbs Per Ft=Average Pounds per foot of    Proppant/Water Slurry on each hydraulic Fracture stage.-   19. Initial Pressure from Fracture=At the initiation of the    hydraulic Fracture.-   20. Avg By Well YME=Average Young's Modulus estimated from well logs    and rock property measurements of cores and cuttings from the    horizontal portion of the well.-   21. Avg PHIE=Average Porosity from the resistivity log.

APPENDIX 3 Glossary of 184 Structured Attributes and List of MoreUnstructured Attributes Used by the MAPPROD Optimizer for CalculatingImportance Weights to Predict Future Production to 97% Accuracy

Geology, Geophysics, Rock Properties, and Reservoir Simulation

Geology=British Thermal Units Target

Geology=Depth

Geophysics=Density

Geophysics=Gas In Place

Geology=Measured Depth Target

Geology=Net To Gross Pay

Geophysics=Permeability_

Geophysics=Gamma_Ray

Geophysics=Porosity

Geophysics=Resistivity

RockProperties=Poissons Ratio

RockProperties=Pore Pressure

RockProperties=Fluid Resistivity

RockProperties=Water Saturation

RockProperties=Temperature Max

RockProperties=Thickness

RockProperties=Total_Organic_Carbon

RockProperties=Total Vertical Depth

RockProperties=Planned Horizontal Length

RockProperties=Latitude

RockProperties=Longitude

RockProperties=Youngs Modulus

ReservoirModeling=Stimulated Reservoir Volume

ReservoirModeling=Simulation Equation

ReservoirModeling=Fracture Conductivity

ReservoirModeling=Fracture Spacing

ReservoirModeling=Net Thickness

ReservoirModeling=Matrix Permeability

ReservoirModeling=Linear Flow Parameter

ReservoirModeling=Normalization Factor

ReservoirModeling=Perforated Lateral Length

ReservoirModeling=Effective Porosity

ReservoirModeling=Initial Gas Saturation

ReservoirModeling=Skin Thickness

ReservoirModeling=Planned Stages

ReservoirModeling=Reservoir Temperature

Drilling

Drilling=Hole Depth

Drilling=Rate Of Penetration

Drilling=Bit Depth

Drilling=Weight on Bit

Drilling=Total Pump Output

Drilling=Rotary Rotations Per Minute

Drilling=Rotary Torque

Drilling=Standpipe Pressure

Drilling=Logging While Drilling Gamma Ray

Drilling=3^(rd) Party Gas

Drilling=Flow Rate

Drilling=Drilling Activity Report

Drilling=Differential Pressure

Drilling=Date and Time Report

LandingPointFeatures=Is In Landing Zone I

LandingPointFeatures=Is In Landing Zone II

LandingPointFeatures=Is In Landing Zone III

LandingPointFeatures=Is In Landing Zone I a

LandingPointFeatures=Is In Landing Zone I b

LandingPointFeatures=Is In Landing Zone I c

LandingPointFeatures=Is In Landing Zone II a

LandingPointFeatures=Is In Landing Zone II b

LandingPointFeatures=Is In Landing Zone II c

LandingPointFeatures=Is In Landing Zone III a

LandingPointFeatures=Is In Landing Zone III b

LandingPointFeatures=Is In Landing Zone III c

LandingPointFeatures=Is In Landing Zone III d

LandingPointFeatures=Is In Landing Zone III e

LandingPointFeatures_=Zone Value Average

LandingPointFeatures_=Zone Value Majority

LandingPointFeatures_=Zone Value Majority Pct

Hydraulic Fracture Completions

FracClassFeatures=FracClass1 Pct

FracClassFeatures=FracClass2 And Above Pct

FracClassFeatures=FracClass2 Pct

FracClassFeatures=FracClass3 And Above Pct

FracClassFeatures=FracClass 3 Pct

FracClassFeatures=FracClass 4 And Above Pct

FracClassFeatures=FracClass4 Pct

FracClassFeatures=FracClass5Pct

FracClassFeatures=FracClassAverage

FracClassFeatures=FracClassMajority

FracClassFeatures=FracClassMajorityPct

FracClassFeatures=FracClassStd

FracClassFeatures=Has FracClass1

FracClassFeatures=Has FracClass2

FracClassFeatures=Has FracClass3

FracClassFeatures=Has FracClass4

FracClassFeatures=Has FracClass5

StressGradientAvgByWell=BaseMD

StressGradientAvgByWell=Vertical Depth

StressGradientAvgByWell=Measured Depth

StressGradientAvgByWell=MWD_Gamma

StressGradientAvgByWell=Pclgrad

StressGradientAvgByWell=PR_c

StressGradientAvgByWell=TopMD

StressGradientAvgByWell=TVD_Perf

StressGradientAvgByWell=X Latitude

StressGradientAvgByWell=Y Longitude

StressGradientAvgByWell=YME_STA

Frac=Volume 100 Mesh Sand Proppant

Frac=Avg Pressure

Frac=Avg Rate

Frac=Avg Sand Per Stage

Frac=Azimuth

Frac=BBLS Per ft

Frac=BD Per ISIP

Frac=Breakdown Pressure

Frac=Breakdown Pressure To Avg Pressure

Frac=Breakdown Rate

Frac=BTU

Frac=BTU Attribute

Frac=Clean Volume

Frac=Cluster Spacing

Frac=Fluid Design

Frac=Fluid Pct Design

Frac=Frac Gradient

Frac=Initial Pressure

Frac=Perfs Per Cluster

Frac=ISIP Instantaneous Shut In Pressure

Frac=Large Proppant Design

Frac=Lateral Length

Frac=Lbs Per BBL

Frac=Lbs Per Ft

Frac=Fracture Ratio

Frac=Max Pressure

Frac=Max PropConcentration

Frac=Max Rate

Frac=Volume Sand 30/50

Frac=SlurryVol

Frac=Small Prop Design

Frac=Number of Stages

Frac=Total Sand Per Well

Frac=UpDip/DownDip

Production

Production=BTU Calculation

Production=Z-Factor

Production=Sample Pressure

Production=Sample Temperature

Production=Molar Component Percent

Production=GPM (Gallons Per Minute) Measure for Condensate

Production=GPM (Gallons Per Minute) Measure for Water

Production=MCF (Thousand Cubic Feet Per Minute) Measure for Gas

Production=Production Estimates Gas

Production=Production Estimates Condensate

Production=Production Estimates Water

Production=Daily Gas

Production=Daily Condensate

Production=Daily Water

Production=Daily Casing Pressure

Production=Daily Tubing Pressure

Production=Daily Pad Volume

Production=Condensate Haul Tickets

Production=Water Haul Tickets

Production=Pad Tank Gauges—Top

Production=Pad Tank Gauges—Bottom

Production=Differential Pressure

Production=ESD (Emergency Shut Down) Alarms

Production=Separator Pressures

Production=Choke Position

Production=LEL (Lower Explosive Limit) Readings

Production=Condensate Density

Production=Water Density

Production=Flash Separation Data

Production=VRU (Vapor Recovery Unit) Data

Production=Battery Voltage data

Production=Other SCADA (Supervisory Control And Data Acquisition)

Pipelines

Pipeline=Pipe Size

Pipeline=Topographical Height

Pipeline=Size Configuration

Pipeline=Fluid and gas composition

Pipeline=Pigging data acquisition

Pipeline=Pigging history

Pipeline=Pigging maintenance schedules

Pipeline=Pigging maintenance type

Pipeline=Pigging maintenance time

Pipeline=Pigging maintenance place

Pipeline=Liquids trapped by location and time

Pipeline=Incidence reports

Pipeline=Repair records

Pipeline=GPS location for each pipeline section

Pipeline=Compressor stations

Pipeline=Pressure

Pipeline=Equipment

Pipeline=Engines

Pipeline=Separators

Pipeline=Compressor station tank level

Pipeline=Other SCADA data

Additional Unstructured Textual Data

Mechanical tool information

Well completion log and schematics (est. start, customer, well, sub-PSL,job BOM, sales order, job status, assigned, archived)

Mechanical well files that describe what was put in the wellbore tomechanically complete the well and at what depth were these tools placed

Sales order that connect to details and part numbers

Part numbers connect to technical limits of tools

Job log (operator, data/time, activity, remarks, job number, primaryBOM, sold to, bill to, plant, Purchase Order/Authorization ForExpenditure, ship to, description, address, details, well ID, etc.)

1-19. (canceled)
 20. A method for optimizing efficiency and gatheringdata from at least one energy source using an energy analytics learningmachine system to maximize a payout from said at least one energy sourcewhile minimizing costs, for the producer, transporter, and/or consumer.collecting structured digital energy data and unstructured textualenergy data from said at least one energy source, said at least oneenergy source being a real or conceived physical energy source;receiving an incoming data stream over a communications network andstoring the incoming data into a systems integration database by aprocessor based server or cloud based distribution of servers to providecollected energy data, the incoming data comprising digital exogenousdata, real-time and historical endogenous data, historical data fromsurrounding physical or interrelated energy sources, and time-lapseprogress, status and maintenance from new data sources over timeincluding from public and private data sources; recording a3-dimensional spatial location and time-lapse 4-dimensional time-seriesfor each data set of the collected energy data; cleaning the collectedenergy data to eliminate extraneous and noisy data; normalizing andstoring the clean collected energy data; processing the normalizedenergy data to determine clusters of correlation in multi-dimensionalspace to simultaneously identify a machine learned ranking of importanceweights for each attribute of said at least one energy source; rankingsaid importance weights, and identifying patterns to enhance the valueof the collected energy data and the normalized energy data; performingpredictive and prescriptive optimization on the normalized energy datautilizing unique combinations of machine learning and statisticalalgorithm ensembles, including at least two of the following: linear andnon-linear support vector machines and support vector regressions,decision trees, hidden Markov models, random forests, neural networks,deep learning networks, bagging, boosting, feature selection,clustering, approximate and dynamic programming; classifyingunstructured textual energy data to correlate with optimal performanceby utilizing progressive clustering with learned seeds, informationextraction and retrieval, image recognition, textual mining, keyword andkey phrase extraction, semantic and sentiment analysis, entity andpattern recognition and knowledge discovery processing to capture thedynamics of said at least one energy source of physically real ortheoretically calculated system to provide categorization results fromlabeled data sets to identify energy production and consumptionpatterns; displaying data and analyses, transmitting recommendations,and receiving actual field actions and reactions on a graphical userinterface on a network-enabled processing device over a communicationsnetwork, the recommendations being based on the collected energy datafrom said at least one energy source, or one or more predictedconditions, communications with the one or more of the real ortheoretical energy systems, autonomous and personalized to steerdisparate data simultaneously to interpreters working on field ortheoretical energy operations that are needed to improve futureperformance from said at least one energy source in response to one ormore trends, said one or more predicted conditions, or recommendationsdisplayed on the graphical user interface connected to the energyanalytics learning machine system; and wherein the energy analyticslearning machine system utilizes: an exploration synthesizer ofavailable data from data sources, in order to score and rank thecombined importance weights of attributes to predict maximum performanceat minimum costs when convolved with specific attributes of said atleast one energy source; a real-time processor to: convolve importanceweight values of attributes received by the energy analytics learningmachine system from historical energy data and attribute data from eachnew energy source as it progresses in real time to predict futureperformance of said each new energy source before actual results aredelivered to the energy analytics learning machine system; and utilizethe 4-dimensional time-series attributes during each time-lapse stage toautomatically classify performance effectiveness of said each time-lapsestage and provide recommendations to maximize future performance of saideach new energy data source; and wherein the recommendations aredirected autonomously to optimize the performance of said at least oneenergy source while minimizing costs over time.
 21. The method of claim20, wherein said at least one energy source being optimized is at leastone of production, distribution and consumption of at least one of thefollowing: oil, natural gas, liquid natural gas (LNG), and electricitygenerated by power plants, the power plants being nuclear, oil, coal,natural gas, solar, hydroelectric or wind.
 22. The method of claim 20maximizes a payout from said at least one energy source while minimizingcosts to at least one of the following: a producer, a transporter, arefiner and a consumer.
 23. The method of claim 20, wherein all energysources work within a similar though separately managed markets andregulators, said all energy sources being either co-located or locatedin different cities, counties, states or countries.
 24. The method ofclaim 20, further comprising the steps of receiving data from digitalfield devices into the systems integration database; combining thereceived data with real time exogenous data comprising weatherforecasts; feeding the historical data and the real-time data into adata cleaning system to recognize a quality of the combination with thereceived data from a comparison with historical performance of at leastone of each digital field device and a data stream.
 25. The method ofclaim 20, further comprising the steps of: determining clusters of likecorrelations in one or more conditions that will likely result in a moreproductive energy source using the energy analytics learning machinesystem; generating from machine learning predicted production,transportation or consumption volumes of said at least one energy sourceover time; displaying identified trends and predicted production,transportation or consumption conditions; alerting an operator when ananomaly between the predicted production, transportation or consumptionconditions and observed field conditions arise to modify and report amodification of an least one of estimated ultimate optimization ofefficiency, profitability and recovery from the energy analyticslearning machine system; and wherein the energy analytics learningmachine system has a coverage of multiple aspects in the analytics,including: at least one of the following regressions: linear regression,support vector regression, classification, regression trees and randomforests; at one of the following classifications: logistic regression,support vector machine and support vector regression, nearest neighbors,decision trees and random forest, neural networks and deep learningnetworks; at least one of the following clustering methods: k-means,k-medoids, expectation-maximization, agglomerative clustering, andnonparametric Bayesian models; at least one of the following featureselection and feature engineering processes: information gain,chi-square, principle component analysis, and filter and wrapper featureselection methods; at least one the following ensemble methods andmodels: bagging, boosting, gradient boosting machine, and randomforests; at least one of the following time series analyses:autoregressive integrated moving average (ARIMA), generalizedautoregressive conditional heteroskedasticity (GARCH), multivariate timeseries analysis, hidden Markov models, nonparametric Bayesian models;and at least one of the following large-scale or big data analyses:MapReduce, approximation, and locality sensitivity hashing.
 26. Themethod of claim 25, further comprising the step of recommending acessation or abandonment of production, distribution or consumption ofsaid at least one energy in response to a determination by the energyanalytics learning machine system that anomalous conditions cannot beeconomically corrected.
 27. The method of claim 20, further comprisingthe steps of receiving at least one of historical exogenous data,real-time exogenous data and the real-time endogenous data of said atleast one energy source over a secure wireless or wired network, andwherein the historical exogenous data and the real-time exogenous datainclude at least one of historical weather data, forecast weather data,and production data from surrounding energy sources under similarhistorical conditions; and computing forecast of future product for saidat least one energy source.
 28. The method of claim 27, furthercomprising querying one or more systems integration databases ofmultiple surrounding energy sources in an area or querying oneintegrated master systems integration database comprising regionallyrelevant geologic and geophysical data, the historical exogenous data,the real-time exogenous data, and the real-time endogenous data toforecast production of said at least one energy source.
 29. The methodof claim 20, wherein the exploration synthesizer of the energy analyticslearning machine system utilizes a support vector regression to estimaterelative importance weights of attributes inputted into the energyanalytics learning machine system and a linear regression to assign apositive or negative correlation sign to product for each weight; andwherein the attributes comprise: relevant geological and geophysicaldata; combining parameters of the support vector regression and linearregression to enable construction of tornado diagrams representingvisually the importance weights of each attribute that correlates with apositive production prediction result and the importance weights of eachattribute that correlates with a negative production prediction resultfor all energy sources in the area or play.
 30. The method of claim 29,wherein the real-time processor convolves f and g, where f is theimportance weight values of attributes computed by the energy analyticslearning machine system from historical data from all energy sources inthe area or play and g is each attribute value specific to an energysource as it progresses; and wherein f*g is an integral transform of aproduct of two functions as attributes specific to said energy source,and the integral transform predicts the future production of said energysource, before commencement of said at least one energy source.
 31. Themethod of claim 20, further comprising step of managing one or moreprescriptive analytics calculations to maximize production of said atleast one energy source while minimizing the costs by the explorationsynthesizer by: computing multiple learning models operatively coupledto the systems integration database and receiving the collected energydata from the field in real time in an exit poll like voting procedureby the energy analytics learning machine system; generating at least onepredicted condition by the energy analytics learning machine system; andstoring resulting changes in operations in the system integrationdatabase from field operations in response to a recommended action. 32.The method of claim 20, further comprising the steps of computing aforecast for production of said at least one energy source by thereal-time processor of the energy analytics learning machine system fora duration of a productive history of said at least one energy source,before commencement of said at least one energy source; continuouslymonitoring and updating the production as said at least one energysource ages by the real-time processor; and providing an estimatedultimate recovery modification recommendations by the real-timeprocessor when a deviation from a forecasted, estimated ultimaterecovery is predicted.